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2878 The study will inform the development of a systems model(s) of the social ecology of traffic safety to test intervention effectiveness in reducing motor-vehicle crashes, injuries, and deaths for the State of Texas by accomplishing the following three objectives: (1) analyze the traffic safety goals proposed in the Texas Department of Transportation’s Highway Safety Plan for 2016 from a systems perspective; (2) assess the applicability of different systems modeling methods suited to analyze the causal relationships and effectiveness of interventions; and, (3) develop preliminary recommendations for a systems model(s) of traffic integrating the conditions and relationships perpetuating motor-vehicle crashes, injuries, deaths, and their potential interventions. The study will provide the fields of traffic safety, bioinformatics, epidemiology, biostatistics, behavioral, human factors, and engineering research with a better understanding of the dynamics driving motor-vehicle crash injuries and deaths to (a) improve crash and injury outcomes and quality of life; (b) decrease spending and/or use of those that are ineffective and increase use of those that are; and, (c) increase understanding of the causes and the outcomes of motor-vehicle crashes, injuries, and deaths individually, socially, culturally, and economically. Collectively, this enables previously impracticable prevention efforts and is a novel way for assessing the effectiveness of different interventions aimed at reducing motor-vehicle-related morbidity and mortality. Systems approaches are capable of capturing the dynamic complexity inherent within traffic and social systems in ways traditional approaches cannot. This analysis will involve identifying suitable systems approaches for analyzing relationships between the traffic system and interventions, including traditional countermeasures to reduce crash and injury morbidity and mortality, such as Texas traffic policies and regulations for motor-vehicles (e.g., speed limits, licensing and educational requirements for motor-vehicle drivers, road geometry and material requirements, safety belt requirements; indicators of motor-vehicle crashes, injuries, and deaths (e.g., morbidity and mortality data for accidents that involve alcohol, drugs, intersections, large trucks, and pedestrians); and, proposed interventions for increasing the use of such practices (e.g., incentives driving use—or lack thereof—of motorcycle safety gear, monetary discounts for safety training programs). While policy makers, economists, and other constituents have proposed specific goals or targets to decrease motor vehicle injuries, crashes, and deaths, none have been tested using methods that capture the dynamic complexity of real-world social systems to not only understand how and why these problems occur, but also what are the best leverage points for change given the effect and cost of the proposed solutions. Accordingly, the systems model to be developed could be used to conduct virtual experiments to test whether the goals set in the Texas Department of Transportation’s Highway Safety Plan for 2016 would be better targeted at one or two specific populations or applied more generally across the state but respective to important social, policy, and environmental factors. If a targeted approach was to be used, the model could help identify which populations or environments exhibit initial conditions favoring adoption of a proposed intervention(s) and hence are the best targets for the intervention. Ultimately, the study seeks to create an optimal portfolio of motor-vehicle safety interventions for use by state and local governments to address the need for truly effective interventions to reduce motor-vehicle crash and injury morbidity and mortality. The model will fulfill a significant need within traffic safety, bioinformatics, epidemiology, biostatistics, behavioral, human factors, and engineering research, as it provides a novel way to assess proposed solutions for reducing motor-vehicle crashes, injuries, and deaths through a means capable of capturing dynamic interactions, adaptivity, and non-linearity inherent within traffic and social systems, that are less time-consuming, and far less costly than traditional approaches.